OpenRaiser/Intern-Atlas huggingface dataset:
Throughput Analysis of UAV-assisted CellularNetworks by Matérn Hardcore Point Process
Venue: arXiv cs.NI 2021
Year: 2021

Unmanned aerial vehicles (UAVs) are expected to coexist with conventional terrestrial cellular networks and become an important component to support high rate transmissions. This paper presents an analytical framework for evaluating the throughput performance of a downlink two-tier heterogeneous network. Considering the minimum distance constraint among UAVs, Matérn hardcore point process (MHP) is utilized to model the locations of UAVs. The locations of terrestrial base stations (BSs) are modeled by Poisson point process (PPP). Tools of stochastic geometry are invoked to derive tractable expressions for average data rates of users. With the analytical results, we discuss the optimal combinations of UAVs' height and power control factor. The result shows that an appropriate power control factor can effectively maximize UAV users' average data rate as well as guaranteeing the BS users' performance under our proposed model.
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Convolutional neural networks typically perform poorly when the test (target domain) and training (source domain) data have significantly different distributions. While this problem can be mitigated by using the target domain data to align the source and target domain feature representations, the target domain data may be unavailable due to privacy concerns. Consequently, there is a need for methods that generalize well despite restricted access to target domain data during training. In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module. Since the segmentation performance varies across different classes, we design a semantic-conditioned style hallucination module to generate affine transformation parameters from semantic information in the segmentation probability maps of the source domain image. Unlike previous adaptation approaches, which treat all classes equally, ASH considers the class-wise differences. The segmentation module and the hallucination module compete adversarially, with the hallucination module generating increasingly "difficult" stylized images to challenge the segmentation module. In response, the segmentation module improves as it is trained with generated samples at an appropriate class-wise difficulty level. Our results on the Cityscapes and Mapillary benchmark datasets show that our method is competitive with state of the art work. Code is made available at https://github.com/gabriel-tjio/ASH.
Contention-based Grant-free Transmission with Extremely Sparse Orthogonal Pilot Scheme
Venue: arXiv cs.IT 2021
Year: 2021

Due to the limited number of traditional orthogonal pilots, pilot collision will severely degrade the performance of contention-based grant-free transmission. To alleviate the pilot collision and exploit the spatial degree of freedom as much as possible, an extremely sparse orthogonal pilot scheme is proposed for uplink grant-free transmission. The proposed sparse pilot is used to perform active user detection and estimate the spatial channel. Then, inter-user interference suppression is performed by spatially combining the received data symbols using the estimated spatial channel. After that, the estimation and compensation of wireless channel and time/frequency offset are performed utilizing the geometric characteristics of combined data symbols. The task of pilot is much lightened, so that the extremely sparse orthogonal pilot can occupy minimized resources, and the number of orthogonal pilots can be increased significantly, which greatly reduces the probability of pilot collision. The numerical results show that the proposed extremely sparse orthogonal pilot scheme significantly improves the performance in high-overloading grant-free scenario.
On the nonexistence of FO-continuous path and tree-decompositions
Venue: arXiv cs.LO 2021
Year: 2021

Bojanczyk and Pilipczuk showed in their celebrated article "Definability equals recognizability for graphs of bounded treewidth" (LICS 2016) that monadic second-order logic can define tree-decompositions in graphs of bounded treewidth. This raises the question whether such decompositions can already be defined in first-order logic (FO). We start by introducing the notion of tree-decompositions of bounded span, which restricts the diameter of the subtree consisting of the bags containing a same node of the structure. Having a bounded span is a natural property of tree-decompositions when dealing with FO, since equality of nodes cannot in general be recovered in FO when it doesn't hold. In particular, it encompasses the notion of domino tree-decompositions. We show that path-decompositions of bounded span are not FO-continuous, in the sense that there exist arbitrarily FO-similar graphs of bounded pathwidth which do not possess FO-similar path-decompositions of bounded span. Then, we show that tree-decompositions of bounded span are not FO-continuous either.
A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021
Venue: arXiv cs.CL 2021
Year: 2021

We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
Venue: arXiv cs.SI 2021
Year: 2021

Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental problem for the full understanding of complicated interactions. The development of graph neural network (GNN) has greatly advanced the analysis of ordinary graphs with pair-wise relations. However, these methods could not be easily extended to the case of hypergraph. In this paper, we generalize the challenges of GNN in representing higher-order data in principle, which are edge- and node-level ambiguities. To overcome the challenges, we present SNALS that utilizes bipartite graph neural network with structural features to collectively tackle the two ambiguity issues. SNALS captures the joint interactions of a hyperedge by its local environment, which is retrieved by collecting the spectrum information of their connections. As a result, SNALS achieves nearly 30% performance increase compared with most recent GNN-based models. In addition, we applied SNALS to predict genetic higher-order interactions on 3D genome organization data. SNALS showed consistently high prediction accuracy across different chromosomes, and generated novel findings on 4-way gene interaction, which is further validated by existing literature.
Exploration and preference satisfaction trade-off in reward-free learning
Venue: arXiv cs.AI 2021
Year: 2021

Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of behaviour that lead to predictable states -- necessary for survival. In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction. For this, we introduce a model-based Bayesian agent equipped with a preference learning mechanism (pepper) using conjugate priors. These conjugate priors are used to augment the expected free energy planner for learning preferences over states (or outcomes) across time. Importantly, our approach enables the agent to learn preferences that encourage adaptive behaviour at test time. We illustrate this in the OpenAI Gym FrozenLake and the 3D mini-world environments -- with and without volatility. Given a constant environment, these agents learn confident (i.e., precise) preferences and act to satisfy them. Conversely, in a volatile setting, perpetual preference uncertainty maintains exploratory behaviour. Our experiments suggest that learnable (reward-free) preferences entail a trade-off between exploration and preference satisfaction. Pepper offers a straightforward framework suitable for designing adaptive agents when reward functions cannot be predefined as in real environments.
Contrastive Representation Learning for Hand Shape Estimation
Venue: arXiv cs.CV 2021
Year: 2021

This work presents improvements in monocular hand shape estimation by building on top of recent advances in unsupervised learning. We extend momentum contrastive learning and contribute a structured collection of hand images, well suited for visual representation learning, which we call HanCo. We find that the representation learned by established contrastive learning methods can be improved significantly by exploiting advanced background removal techniques and multi-view information. These allow us to generate more diverse instance pairs than those obtained by augmentations commonly used in exemplar based approaches. Our method leads to a more suitable representation for the hand shape estimation task and shows a 4.7% reduction in mesh error and a 3.6% improvement in F-score compared to an ImageNet pretrained baseline. We make our benchmark dataset publicly available, to encourage further research into this direction.
Interpolation and Model Checking for Nonlinear Arithmetic
Venue: arXiv cs.LO 2021
Year: 2021

We present a new model-based interpolation procedure for satisfiability modulo theories (SMT). The procedure uses a new mode of interaction with the SMT solver that we call solving modulo a model. This either extends a given partial model into a full model for a set of assertions or returns an explanation (a model interpolant) when no solution exists. This mode of interaction fits well into the model-constructing satisfiability (MCSAT) framework of SMT. We use it to develop an interpolation procedure for any MCSAT-supported theory. In particular, this method leads to an effective interpolation procedure for nonlinear real arithmetic. We evaluate the new procedure by integrating it into a model checker and comparing it with state-of-art model-checking tools for nonlinear arithmetic.
countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter
Venue: arXiv cs.DS 2021
Year: 2021

Bloom Filter is a probabilistic data structure for the membership query, and it has been intensely experimented in various fields to reduce memory consumption and enhance a system's performance. Bloom Filter is classified into two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter. CBF has a higher false positive probability than standard Bloom Filter (SBF), i.e., CBF uses a higher memory footprint than SBF. But CBF can address the issue of the false negative probability. Notably, SBF is also false negative free, but it cannot support delete operations like CBF. To address these issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom Filter, called countBF. countBF uses a modified murmur hash function to enhance its various requirements, which is experimentally evaluated. Our experimental results show that countBF uses $1.96\times$ and $7.85\times$ less memory than SBF and CBF respectively, while preserving lower false positive probability and execution time than both SBF and CBF. The overall accuracy of countBF is $99.999921$, and it proves the superiority of countBF over SBF and CBF. Also, we compare with other state-of-the-art counting Bloom Filters.
Proof methods for robust low-rank matrix recovery
Venue: arXiv cs.IT 2021
Year: 2021

Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the potential and the limitations of low-rank matrix models in sensing problems. In this article, we compare the two main proof techniques that have been paving the way to a rigorous analysis, discuss their potential and limitations, and survey their successful applications. On the one hand, we review approaches based on descent cone analysis, showing that they often lead to strong guarantees even in the presence of adversarial noise, but face limitations when it comes to structured observations. On the other hand, we discuss techniques using approximate dual certificates and the golfing scheme, which are often better suited to deal with practical measurement structures, but sometimes lead to weaker guarantees. Lastly, we review recent progress towards analyzing descent cones also for structured scenarios -- exploiting the idea of splitting the cones into multiple parts that are analyzed via different techniques.
Asymmetrical Bi-RNN for pedestrian trajectory encoding
Venue: arXiv cs.CV 2021
Year: 2021

Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents. In this article, we present an asymmetrical bidirectional recurrent neural network architecture called U-RNN to encode pedestrian trajectories and evaluate its relevance to replace LSTMs for various forecasting models. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant yields better results regarding every available metrics (ADE, FDE, Collision rate) than common trajectory encoders for a variety of approaches and interaction modules, suggesting that the proposed approach is a viable alternative to the de facto sequence encoding RNNs. Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is available at: github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories
SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning
Venue: arXiv cs.CV 2021
Year: 2021

Modifications on triplet loss that rescale the back-propagated gradients of special pairs have made significant progress on local descriptor learning. However, current gradient modulation strategies are mainly static so that they would suffer from changes of training phases or datasets. In this paper, we propose a dynamic gradient modulation, named SDGMNet, to improve triplet loss for local descriptor learning. The core of our method is formulating modulation functions with statistical characteristics which are estimated dynamically. Firstly, we perform deep analysis on back propagation of general triplet-based loss and introduce included angle for distance measure. On this basis, auto-focus modulation is employed to moderate the impact of statistically uncommon individual pairs in stochastic gradient descent optimization; probabilistic margin cuts off the gradients of proportional Siamese pairs that are believed to reach the optimum; power adjustment balances the total weights of negative pairs and positive pairs. Extensive experiments demonstrate that our novel descriptor surpasses previous state-of-the-arts on standard benchmarks including patch verification, matching and retrieval tasks.
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals. We find substantial failure cases which reveal that current VQA systems are still brittle. Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations.
Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language
Venue: arXiv cs.CL 2021
Year: 2021

Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, we have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. We have examined the performance of our proposed models from different perspective. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.
New Competitive Semi-online Scheduling Algorithms for Small Number of Identical Machines
Venue: arXiv cs.DS 2021
Year: 2021

Design and analysis of constant competitive deterministic semi-online algorithms for the multi-processor scheduling problem with small number of identical machines have gained significant research interest in the last two decades. In the semi-online scheduling problem for makespan minimization, we are given a sequence of independent jobs one by one in order and upon arrival, each job must be allocated to a machine with prior knowledge of some Extra Piece of Information (EPI) about the future jobs. Researchers have designed multiple variants of semi-online scheduling algorithms with constant competitive ratios by considering one or more EPI. In this paper, we propose four new variants of competitive deterministic semi-online algorithms for smaller number of identical machines by considering two EPI such as Decr and Sum. We obtain improved upper bound and lower bound results on the competitive ratio for our proposed algorithms, which are comparable to the best known results in the literature. In two identical machines setting with known Sum, we show a tight bound of 1.33 on the competitive ratio by considering a sequence of equal size jobs. In the same setting we achieve a lower bound of 1.04 and an upper bound of 1.16 by considering Sum and a sequence of jobs arriving in order of decreasing sizes. For three identical machines setting with known Decr and Sum, we show a lower bound of 1.11 on the competitive ratio. In this setting, we obtain an upper bound of 1.5 for scheduling a sequence of equal size jobs and achieves an upper bound of 1.2 by considering a sequence of decreasing size jobs. Further we develop an improved competitive algorithm with an upper bound of 1.11 on the competitive ratio.
Predicting the Success of Domain Adaptation in Text Similarity
Venue: arXiv cs.CL 2021
Year: 2021

Transfer learning methods, and in particular domain adaptation, help exploit labeled data in one domain to improve the performance of a certain task in another domain. However, it is still not clear what factors affect the success of domain adaptation. This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. We use descriptive domain information and cross-domain similarity metrics as predictive features. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.
Comprehension Based Question Answering using Bloom's Taxonomy
Venue: arXiv cs.CL 2021
Year: 2021

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Venue: arXiv cs.LG 2021
Year: 2021

Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts' trust in the AI agents. Explainable AI may build such trust by helping medical experts to understand the AI decision processes behind diagnostic judgements. Here we introduce and evaluate explanations based on Bayesian Teaching, a formal account of explanation rooted in the cognitive science of human learning. We find that medical experts exposed to explanations generated by Bayesian Teaching successfully predict the AI's diagnostic decisions and are more likely to certify the AI for cases when the AI is correct than when it is wrong, indicating appropriate trust. These results show that Explainable AI can be used to support human-AI collaboration in medical imaging.
Does class size matter? An in-depth assessment of the effect of class size in software defect prediction
Venue: arXiv cs.SE 2021
Year: 2021

In the past 20 years, defect prediction studies have generally acknowledged the effect of class size on software prediction performance. To quantify the relationship between object-oriented (OO) metrics and defects, modelling has to take into account the direct, and potentially indirect, effects of class size on defects. However, some studies have shown that size cannot be simply controlled or ignored, when building prediction models. As such, there remains a question whether, and when, to control for class size. This study provides a new in-depth examination of the impact of class size on the relationship between OO metrics and software defects or defect-proneness. We assess the impact of class size on the number of defects and defect-proneness in software systems by employing a regression-based mediation (with bootstrapping) and moderation analysis to investigate the direct and indirect effect of class size in count and binary defect prediction. Our results show that the size effect is not always significant for all metrics. Of the seven OO metrics we investigated, size consistently has significant mediation impact only on the relationship between Coupling Between Objects (CBO) and defects/defect-proneness, and a potential moderation impact on the relationship between Fan-out and defects/defect-proneness. Based on our results we make three recommendations. One, we encourage researchers and practitioners to examine the impact of class size for the specific data they have in hand and through the use of the proposed statistical mediation/moderation procedures. Two, we encourage empirical studies to investigate the indirect effect of possible additional variables in their models when relevant. Three, the statistical procedures adopted in this study could be used in other empirical software engineering research to investigate the influence of potential mediators/moderators.